Cleaning processing method and device for bank business submitted data and storage medium

Abstract

The invention relates to the technical field of banking business data processing, in particular to a cleaning processing method and device for banking business submitted data and a storage medium. The method comprises the following steps: collecting multi-service system data; preprocessing a data source format, a data dictionary and a field type based on the multi-service system data; based on the multi-service system data, client types are judged, and the client types comprise enterprise clients and natural person clients; according to a preset service rule, the multi-service system data is deeply cleaned, and the service rule comprises missing value processing, error data correction, associated data item processing assignment and data encryption; after deep cleaning is completed, the multi-service system data are verified, and verification comprises accounting total score verification and service rule verification. According to the method, through format processing, data cleaning, verification and auditing, the accuracy and efficiency of bank submitted data processing are effectively improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of banking business data processing, and in particular, to a method, device and storage medium for cleaning and processing banking business reporting data. Background Art

[0002] With the diversification of banking business, various business systems continue to emerge, such as savings business system, credit business system, credit card business system, etc. These systems generate massive amounts of data in daily operations, but due to different construction periods and different standards, there are many problems with the data:

[0003] Confusing data source formats: Different systems use different date formats, number formats, and coding methods, making it difficult to directly integrate data when aggregating, which can easily lead to misunderstandings and misinterpretations. For example, some systems record dates in "MM / DD / YYYY", while others use "YYYY-MM-DD", which greatly hinders cross-system data correlation analysis.

[0004] Data duplication problem: When multiple business systems run in parallel, there is a lack of a unified data deduplication mechanism, which makes it easy for the same business information to be recorded multiple times in different data sources. This not only wastes storage resources, but may also lead to deviations in statistical analysis results and mislead business decisions.

[0005] Inconsistent customer information: For natural persons and corporate customers, key identification information such as certificate type, number, name, etc. in various business systems is not entered in a standardized or synchronized manner, making it difficult to accurately identify and uniformly manage customer identities, affecting the accuracy of customer relationship maintenance and risk assessment.

[0006] Poor data accuracy: There are often missing values ​​and erroneous values ​​in business data, such as missing customer occupation and income information, and incorrect recording of transaction amounts, which makes the business reports and risk models based on these data unreliable and unable to provide strong support for bank operations.

[0007] Data security risks: Banking business data contains a large amount of customer privacy and business secrets. If it is directly stored or transmitted without proper processing, it is extremely vulnerable to external attacks and leaks, causing serious damage to customer rights and interests and the bank's reputation.

[0008] In summary, there is an urgent need for an efficient and comprehensive data cleaning and processing solution to improve the quality of banking business reporting data and ensure smooth business operation and compliance development. Summary of the invention

[0009] One purpose of the embodiments of the present application is to provide a method, device and storage medium for cleaning and processing banking business reporting data, aiming to solve the technical problems that banking business data is complicated, of uneven quality, and difficult to meet regulatory requirements and internal precise analysis needs.

[0010] In a first aspect, an embodiment of the present application provides a method for cleaning banking business reporting data, including:

[0011] Collect multi-business system data, which can be obtained by directly connecting to the business system database and reading TXT files;

[0012] Based on the multi-business system data, preprocessing is performed on the data source format, data dictionary, and field type; the preprocessing includes: ensuring the uniqueness of the data by defining a primary key constraint to prevent repeated insertion of data;

[0013] Based on the multi-business system data, determine the customer type, which includes corporate customers and natural person customers; if the customer type is the corporate customer, verify whether the certificate name of the corporate customer is consistent, and merge them into the same customer when the certificate names are consistent; if the customer type is the natural person customer, verify whether the natural person customer information registered in each business system is consistent, and merge them into the same customer when the natural person customer information is consistent, wherein the natural person customer information includes certificate type, certificate number and certificate name;

[0014] Deeply clean the multi-business system data according to preset business rules, which include missing value processing, erroneous data correction, associated data item processing and assignment, and data encryption;

[0015] After completing the deep cleaning, the multi-business system data is verified, and the verification includes account total and sub-account verification and business rule verification. The account total and sub-account verification is used to verify whether the amounts in the general ledger and sub-account of bank transaction data within the reporting period are consistent. The business rule verification includes length verification, data dictionary verification, non-empty verification, association verification, and numerical comparison verification.

[0016] In combination with the first aspect, in a possible implementation manner, the primary key constraint is defined by, for a simple business table, using one field as the primary key; and for a complex business table, using multiple fields as a composite primary key.

[0017] In combination with the first aspect, in one possible implementation, if the ID names of the corporate customers are inconsistent, and / or the ID type, ID number, and ID name of the natural person customer information are not completely consistent, at least two customer numbers are generated respectively to correspond to each corporate customer and / or each natural person customer.

[0018] In combination with the first aspect, in a possible implementation, when processing missing values, if the exact missing value cannot be obtained from other valued fields, it is filled in according to a preset default value or rule.

[0019] In combination with the first aspect, in a possible implementation, when performing processing and assigning values ​​to associated data items, if the associated data required for calculation is missing, an estimate is made based on other relevant data or a value is assigned according to a preset rule.

[0020] In combination with the first aspect, in a possible implementation method, the transformation rule of the personal ID number is: take the first three bytes of the name and concatenate them with the ID number to obtain a character string, encrypt the character string using the SM3 encryption algorithm, take the first 6 bytes of the UTF-8 encoding of the ID number (if less than 6 digits are added from the left, 0 to 6 digits are added) and concatenate the encrypted 64-bit characters to obtain the encrypted ID number.

[0021] In combination with the first aspect, in a possible implementation, the natural person's name is transformed in such a way that the last Chinese character is retained in the database, and the company name is not encrypted.

[0022] In combination with the first aspect, in a possible implementation, the business serial number is deformed in the form of encrypting the core transaction date concatenated with the transaction entry number to store the correspondence between the real business serial number and the entry number in the business system database.

[0023] In a second aspect, an embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory. When the processor executes the one or more computer programs, the electronic device implements a method as described in any one of the first aspects above.

[0024] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes any of the methods described above.

[0025] The embodiments of the present application can achieve the following technical effects:

[0026] This application reduces data errors and ensures data accuracy by unifying the data source format, standardizing the data dictionary, and accurately verifying field types. For example, it makes business analysis based on time series more accurate; it uses primary key constraints and deduplication alarms to eliminate duplicate data and ensure the authenticity of business indicator statistics; it accurately verifies natural person customer information and corporate customer information to further achieve customer identity unification and improve customer management and risk monitoring efficiency.

[0027] The method proposed in this application can solve the problem of integrating data from multiple business systems and accurately identifying the data of the same customer; it can solve the problem of leakage in production data transmission; and it can process erroneous, missing, duplicated, inconsistent or incorrectly formatted data. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0029] Figure 1 A flowchart of a method for cleaning and processing banking business reporting data provided in an embodiment of the present application;

[0030] Figure 2 It is a structural schematic diagram of a cleaning processing device for bank business reporting data provided by an embodiment of the present application;

[0031] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0033] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other, all within the scope of protection of the present application. In addition, although the functional module division is performed in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a sequence different from the module division in the device or the flow chart. Furthermore, the words "first", "second", "third", etc. used in this application do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0034] In the first place, see Figure 1 The present application embodiment provides a method for cleaning and processing banking business reporting data, the method comprising:

[0035] Step S10, collecting multi-business system data, wherein the multi-business system data supports acquisition by directly connecting to the business system database and reading a TXT file;

[0036] Step S20, based on the multi-business system data, preprocessing the data source format, data dictionary, and field type; the preprocessing includes: ensuring the uniqueness of the data by defining a primary key constraint to prevent repeated insertion of data;

[0037] Step S30, based on the multi-business system data, determine the customer type, the customer type includes corporate customers and natural person customers; if the customer type is the corporate customer, check whether the certificate name of the corporate customer is consistent, and merge them into the same customer when the certificate name is consistent; if the customer type is the natural person customer, check whether the natural person customer information registered in each business system is consistent, and merge them into the same customer when the natural person customer information is consistent, wherein the natural person customer information includes certificate type, certificate number and certificate name;

[0038] Step S60, deep cleaning the multi-business system data according to preset business rules, wherein the business rules include missing value processing, error data correction, associated data item processing and value assignment, and data encryption;

[0039] Step S70, after completing the deep cleaning, the multi-business system data is verified, the verification includes account total and sub-account verification and business rule verification, the account total and sub-account verification is used to verify whether the amounts of the general ledger and sub-account of bank transaction data within the reporting period are consistent, and the business rule verification includes length verification, data dictionary verification, non-empty verification, association verification, and numerical comparison verification.

[0040] In this embodiment, two flexible and efficient methods are supported to obtain multi-service system data, specifically, including:

[0041] Direct connection to business system database: With the help of self-developed efficient data connection middleware, a stable and real-time connection channel is established with each business system database. The middleware has a built-in intelligent adaptation function, which can automatically adjust the data extraction strategy and protocol according to the characteristics of different database types (such as Oracle, MySQL, SQL Server, etc.), and accurately capture the required data in strict accordance with the pre-set refined data extraction rules. These rules cover data screening conditions, extraction frequency (which can be set to real-time synchronization, scheduled batch extraction, etc. as needed), ensuring that the latest business dynamics in the source database can be captured in a timely manner, providing a real-time data foundation for subsequent data processing.

[0042] Read TXT files: For business data that is imported externally or partially stored in text form, a powerful text parsing engine is equipped. In the process of reading TXT files, it identifies various format features of the file, including but not limited to row and column structure, custom separators (such as commas, tabs, specific character combinations, etc.), and based on the built-in general and custom format template library, quickly converts disordered text data into a structured, easy-to-process data format, and seamlessly connects to the subsequent cleaning process.

[0043] In this embodiment, after acquiring the multi-business system data, its data source format, data dictionary and field type are processed:

[0044] Source format standardization: After data collection is completed, the format conversion module is immediately started. This module has a built-in large conversion rule library covering common business data formats. It automatically matches and performs corresponding conversion operations for various format inconsistencies such as dates, numbers, character encodings, etc. For example, in the face of date format differences, various input formats can be uniformly converted into the "YYYYMMDD" format that meets the financial industry standards, ensuring the accuracy of date data in subsequent calculations and comparisons.

[0045] Data dictionary integration: An intelligent data dictionary management system has been designed, which can automatically scan the data dictionaries of various business systems, and accurately identify and map fields with the same business meaning but different names or codes through semantic analysis and field association algorithms. For example, the "deposit term code" in the savings business system and the "loan term identifier" in the credit business system are uniformly mapped into the "business term code", achieving seamless connection at the data semantic level.

[0046] Field type verification: Introduce a strict field type verification mechanism, based on the technology of combining static type checking with dynamic data pattern recognition, to perform double verification on character type, numeric type, date type and other fields. Static checking is based on the preset field definition specifications, and initially screens for type errors when data enters; dynamic recognition is based on the actual performance characteristics of the data (such as data value range, character composition rules, etc.) during the data processing process, and promptly corrects type mismatch problems caused by system errors or abnormal data entry.

[0047] In this embodiment, when designing and maintaining a database, a primary key constraint is defined to ensure the uniqueness of data in a table and prevent duplicate insertion of data. When trying to insert or update a row of data, if its primary key value is the same as the primary key value of a row already existing in the table, the database system will reject the operation and throw an error (such as an SQL constraint violation error). This can avoid data duplication and maintain data accuracy and consistency.

[0048] In this embodiment, the idea of ​​processing missing values ​​is to obtain them from other value fields, such as using the ID card number to complete gender, birthday, etc.; the mobile phone number of the employee table is obtained from the contact information of the account opening customer.

[0049] The idea of ​​correcting erroneous data is to check according to the real business needs of the field. For example, if the fields such as account name and phone number contain special characters, the idea of ​​handling such problems is to assign compliant values ​​according to the customer's registration information in other business tables. If there is conflicting data (for example, a field has different registration contents in different business systems), non-empty compliant data is used first. If all are compliant, the data registered in the core system is used uniformly.

[0050] The idea of ​​associating data items is to process and assign values. For example, the loan status of a loan is calculated by the amount of principal and interest owed. If the amount is greater than 0, it is assigned to unsettled, and if the amount is 0, it is assigned to settled.

[0051] Data encryption is to desensitize production data by encrypting the certificate number, deforming the business serial number, deforming the contract number, etc. For example, the name of a natural person customer is exposed in a limited manner: only the last Chinese character of the Chinese name and the last three characters of the English name are retained; the ID number is desensitized by forming a string with the first Chinese character of the individual's name and the ID number, taking its SM3 hash value, and then concatenating the first six bytes of the ID number.

[0052] It is easy to understand that since the submitted data needs to respond to the supervisor's inquiries and needs to be traced back to the business system to check the registration status, this method uses field-level encryption, and the complexity of the deformation of each field is different. Personal basic information such as ID number, name, contact information, etc. use asymmetric encryption, and other fields such as serial number, contract number, etc. are symmetric encryption, and the key can be obtained through a certain method.

[0053] The deformation rules of personal ID numbers include:

[0054] Take the first three bytes of the name and concatenate it with the ID number to get a string. For example, Zhang San's ID number is 110101199001010001, so the string is Zhang110101199001010001; George Bush's passport number is G12345678, so the string is GeoG12345678;

[0055] The above string is encrypted using the SM3 encryption algorithm, the full name of which is "commercial encryption SM3 hash algorithm", which is a cryptographic hash function standard publicly released by the State Cryptography Administration. It generates 64-bit characters.

[0056] For example, Zhang San:

[0057] 747d3ff3ba19dc632cdc7b572bff69bf3be08140f9b6a1cbeb50cced5fbb8be6George ·Bush: 0bf6934dd98523c50b61789b86a4b16b9d42e5e5a7f37f7e651ae7b7922b34ee

[0058] Take the first 6 bytes of the UTF-8 encoding of the ID number (if it is less than 6 bits, add 0 to 6 bits from the left of the ID number), concatenate the above 64 bits of characters, and get the encrypted ID number.

[0059] For example, Zhang San:

[0060] 110101747d3ff3ba19dc632cdc7b572bff69bf3be08140f9b6a1cbeb50cced5fbb8be6Ge orge·BushG123450bf6934dd98523c50b61789b86a4b16b9d42e5e5a7f37f7e651ae7b7922b34ee

[0061] In this embodiment, the way of transforming the name of a natural person is to keep only the last Chinese character in the database, for example, Zhang San is encrypted as San. The name of the enterprise is not encrypted and needs to be stored normally in accordance with the regulatory reporting requirements.

[0062] In this embodiment, the mobile phone number is transformed in such a way that the number is encrypted using the SM3 algorithm.

[0063] In this embodiment, the business serial number is deformed in the following way: In order to prevent the transaction information from being viewed, tampered with or forged by unauthorized personnel, the method deforms the business transaction serial number: the core transaction date is concatenated with the transaction entry number for encryption, and the comparison between the real business serial number and the entry number is stored in the business system database.

[0064] For example, the real business serial number of the core system is 012345, the corresponding entry number registered in the business system is 56789, and the business occurred on January 1, 2024. The transformed business serial number is: 2024010156789.

[0065] In this embodiment, the contract number is transformed by splicing the following fields: the customer number of the customer business system, the bank code, the year the business was generated, and the order in which the business occurred this year. Specifically, if Zhang San's customer number in the credit system is 123, his bank business code is 2001, and he applied for a loan in 2004, this loan is the 501st business of the bank (a total of 6 digits, and 0 is added on the left if less than 6 digits). Then his contract number is 12320012004000501.

[0066] Furthermore, in this embodiment, the primary key constraint is defined as follows: for a simple business table, one field is used as the primary key; for a complex business table, multiple fields are used as a composite primary key.

[0067] Furthermore, if the certificate names of the corporate customers are inconsistent, and / or the certificate type, certificate number, and certificate name of the natural person customer information are not completely consistent, at least two customer numbers are generated respectively to correspond to each corporate customer and / or each natural person customer.

[0068] Furthermore, in this embodiment, when performing missing value processing, if the exact missing value cannot be obtained from other valued fields, it is filled in according to a preset default value or rule.

[0069] Furthermore, in this embodiment, when performing processing and assigning values ​​to associated data items, if the associated data required for calculation is missing, it is estimated based on other related data or assigned according to preset rules.

[0070] Furthermore, in this embodiment, the transformation rule of the personal ID number is: take the first three bytes of the name and concatenate them with the ID number to obtain a character string, encrypt the character string using the SM3 encryption algorithm, take the first 6 bytes of the UTF-8 encoding of the ID number (if less than 6 digits are added from the left, 0 to 6 digits are added) and concatenate the encrypted 64-bit characters to obtain the encrypted ID number.

[0071] Furthermore, in this embodiment, the natural person's name is transformed in such a way that the last Chinese character is retained in the database, and the company name is not encrypted.

[0072] Furthermore, in this embodiment, the business serial number deformation method is to encrypt it in the form of splicing the core transaction date with the transaction entry number, so as to store the corresponding relationship between the real business serial number and the entry number in the business system database.

[0073] In this embodiment, after the data cleaning is completed, the verification work of the subsequent steps can be carried out. Usually, banks attach the most importance to account transaction data, so they need to pay special attention to the total and sub-account verification, which can verify whether the amount of the bank transaction data general ledger and sub-account is consistent within the reporting period.

[0074] Business rule verification includes length verification (for example, the length is 8 when the collection date is not empty), data dictionary verification (for example, male or female should be filled in when the gender is not empty), non-empty verification (for example, the transaction amount cannot be empty), association verification (for example, the handling teller number in the transaction flow table needs to be reflected in the teller table), and numerical comparison verification (for example, the establishment date of a corporate customer cannot be greater than the collection date and cannot be less than 19490101).

[0075] Manual review is to feed back the current data to bank employees for them to make corrections. For example, if the rating information of a company is missing, they will need to enter the rating information into the business system.

[0076] In the second aspect, the present application also provides a cleaning and processing device for bank business reporting data. Figure 2 This embodiment provides a cleaning processing device for bank business reporting data, including:

[0077] The data collection module 210 is used to collect multi-business system data, and the multi-business system data supports being obtained by directly connecting to the business system database and reading TXT files;

[0078] The data preprocessing module 220 is used to preprocess the data source format, data dictionary, and field type based on the multi-business system data; the preprocessing includes: ensuring the uniqueness of the data by defining a primary key constraint to prevent repeated insertion of data;

[0079] The data integration module 230 is used to determine the customer type based on the multi-business system data, and the customer type includes corporate customers and natural person customers; if the customer type is the corporate customer, then check whether the certificate name of the corporate customer is consistent, and merge them into the same customer when the certificate name is consistent; if the customer type is the natural person customer, then check whether the natural person customer information registered in each business system is consistent, and merge them into the same customer when the natural person customer information is consistent, wherein the natural person customer information includes certificate type, certificate number and certificate name;

[0080] A data cleaning module 240 is used to perform deep cleaning on the multi-business system data according to preset business rules, wherein the business rules include missing value processing, error data correction, associated data item processing and assignment, and data encryption;

[0081] The data verification module 250 is used to verify the multi-business system data after completing the deep cleaning. The verification includes account total and sub-account verification and business rule verification. The account total and sub-account verification is used to verify whether the amounts in the general ledger and sub-account of bank transaction data within the reporting period are consistent. The business rule verification includes length verification, data dictionary verification, non-empty verification, association verification, and numerical comparison verification.

[0082] It should be noted that the above-mentioned cleaning processing device for bank business reporting data can execute the cleaning processing method for bank business reporting data provided in the implementation mode of this application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in the implementation mode of the cleaning processing device for bank business reporting data, please refer to the cleaning processing method for bank business reporting data provided in the implementation mode of this application.

[0083] See also Figure 3 , Figure 3 1 is a schematic diagram of the structure of an electronic device 300 provided in an embodiment of the present application. The electronic device 300 includes one or more processors 31 and a memory 32. The memory 32 is connected to the one or more processors 31, for example, connected to the processor 31 through a bus.

[0084] The processor 31 is configured to support the electronic device 300 to perform the corresponding functions in the method in the above method embodiment. The processor 31 can be a central processing unit (CPU), a network processor (NP), a hardware chip or any combination thereof. The above hardware chip can be an application specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0085] The memory 32 is used to store program codes, etc. The memory may include a volatile memory (VM), such as a random access memory (RAM); the memory may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); the memory 32 may also include a combination of the above-mentioned types of memory.

[0086] The memory 32 can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the bank business reporting data cleaning processing method in the embodiment of the present application. The processor 31 executes the various functional applications and data processing of the bank business reporting data cleaning processing method and the bank business reporting data cleaning processing device by running the non-volatile software programs, instructions and modules stored in the memory 32, that is, realizes the functions of the various modules or units of the bank business reporting data cleaning processing method and the bank business reporting data cleaning processing device provided in the above method embodiment.

[0087] The memory 32 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required by at least one function. The data storage area may store data created according to the use of the cleaning processing device for bank business reporting data, etc. In some embodiments, the memory 32 may optionally include a memory remotely arranged relative to the processor 31, and these remote memories may be connected to the cleaning processing device for bank business reporting data via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0088] One or more modules are stored in the memory 32. When executed by one or more processors 31, the cleaning processing method of the banking business reporting data in any of the above method embodiments is executed, for example, the method steps described in the above method embodiments are executed to realize the functions of the modules described in the above device embodiments.

[0089] An embodiment of the present application further provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the method of the aforementioned embodiment.

[0090] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0091] The above disclosure is only the preferred embodiment of the present application, which certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A method for cleaning and processing banking business reporting data, characterized in that: include: Collect multi-business system data, which can be obtained by directly connecting to the business system database and reading TXT files; Based on the multi-business system data, preprocessing is performed on the data source format, data dictionary, and field type; the preprocessing includes: ensuring the uniqueness of the data by defining a primary key constraint to prevent repeated insertion of data; Based on the multi-business system data, determine the customer type, which includes corporate customers and natural person customers; if the customer type is the corporate customer, verify whether the certificate name of the corporate customer is consistent, and merge them into the same customer when the certificate names are consistent; if the customer type is the natural person customer, verify whether the natural person customer information registered in each business system is consistent, and merge them into the same customer when the natural person customer information is consistent, wherein the natural person customer information includes certificate type, certificate number and certificate name; Deeply clean the multi-business system data according to preset business rules, which include missing value processing, erroneous data correction, associated data item processing and assignment, and data encryption; After completing the deep cleaning, the multi-business system data is verified, and the verification includes account total and sub-account verification and business rule verification. The account total and sub-account verification is used to verify whether the amounts in the general ledger and sub-account of bank transaction data within the reporting period are consistent. The business rule verification includes length verification, data dictionary verification, non-empty verification, association verification, and numerical comparison verification.

2. The method for cleaning and processing banking business reporting data according to claim 1, characterized in that: Specifically, the primary key constraint is defined as follows: for a simple business table, one field is used as the primary key; for a complex business table, multiple fields are used as a composite primary key.

3. A method for cleaning and processing banking business reporting data according to claim 1, characterized in that: The method further comprises: If the certificate names of the corporate customers are inconsistent, and / or the certificate type, certificate number, and certificate name of the natural person customer information are not completely consistent, at least two customer numbers are generated respectively to correspond to each corporate customer and / or each natural person customer.

4. A method for cleaning and processing banking business reporting data according to claim 1, characterized in that: When processing missing values, if the exact missing value cannot be obtained from other valuable fields, it will be filled according to the preset default value or rule.

5. A method for cleaning and processing banking business reporting data according to claim 1, characterized in that: When processing and assigning values ​​to associated data items, if the associated data required for the calculation is missing, it is estimated based on other relevant data or assigned according to preset rules.

6. A banking business reporting data cleaning and processing method according to claim 1, characterized in that: The transformation rule of the personal ID number is: take the first three bytes of the name and concatenate them with the ID number to obtain a character string, encrypt the character string using the SM3 encryption algorithm, take the first 6 bytes of the UTF-8 encoding of the ID number (if less than 6 digits are added from the left to 0 to 6 digits), concatenate the encrypted 64-bit characters and obtain the encrypted ID number.

7. A method for cleaning and processing banking business reporting data according to claim 1, characterized in that: The natural person's name is transformed in such a way that the last Chinese character is retained in the database, and the company name is not encrypted.

8. A method for cleaning and processing banking business reporting data according to claim 1, characterized in that: The business serial number deformation method is to encrypt the core transaction date in the form of splicing the transaction entry number, so as to store the corresponding relationship between the real business serial number and the entry number in the business system database.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the electronic device implements the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 8.

Images